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» Using Machine Learning to Focus Iterative Optimization
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ICML
2010
IEEE
15 years 4 months ago
Learning Efficiently with Approximate Inference via Dual Losses
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Glo...
ECML
2007
Springer
15 years 4 months ago
Sequence Labeling with Reinforcement Learning and Ranking Algorithms
Many problems in areas such as Natural Language Processing, Information Retrieval, or Bioinformatic involve the generic task of sequence labeling. In many cases, the aim is to assi...
Francis Maes, Ludovic Denoyer, Patrick Gallinari
MLDM
2007
Springer
15 years 9 months ago
Kernel MDL to Determine the Number of Clusters
In this paper we propose a new criterion, based on Minimum Description Length (MDL), to estimate an optimal number of clusters. This criterion, called Kernel MDL (KMDL), is particu...
Ivan O. Kyrgyzov, Olexiy O. Kyrgyzov, Henri Ma&ici...
CEC
2005
IEEE
15 years 8 months ago
Relationships between internal and external metrics in co-evolution
Co-evolutionary algorithms (CEAs) have been applied to optimization and machine learning problems with often mediocre results. One of the causes for the unfulfilled expectations i...
Elena Popovici, Kenneth A. De Jong
ICML
2004
IEEE
16 years 3 months ago
Learning Bayesian network classifiers by maximizing conditional likelihood
Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when lear...
Daniel Grossman, Pedro Domingos